The Catfish. The Reject. Attack of the Bots. These aren’t upcoming horror movies – they’re examples of fraud scams experienced by consumers and businesses alike. In the US alone, consumers lost $246 million from online shopping fraud in 2020, according to the FTC. For businesses, the threat is even greater: omni-channel brands and retailers are constantly at risk of data breaches and organized retail crimes.

Fraudsters take advantage of outdated legacy systems and security loopholes, which in turn damages a brand’s reputation, sometimes permanently. Hacked eCommerce sites and stolen payment card numbers are some of the primary reasons customers walk away from brands.

Battling fraud can feel futile. But advancements in AI and machine learning can help organizations be more proactive about securing data and spotting patterns of fraudulent behavior. Let’s look at some examples of fraud, what consumers are worried about, and what a business can do to secure its brand and protect its reputation.

All Industries Report an Increase in Fraudulent Behavior

According to a report from the Association of Certified Fraud Examiners, 79 percent of respondents have observed an increase in fraud since the beginning of the COVID-19 pandemic. 90 percent of respondents anticipate that this trend will continue well into 2021. Specific types of fraud cited were malware and ransomware attacks and credit card fraud. For brands and retailers, credit card and payment fraud, both by individuals and organized retail criminals, is an urgent issue.

Retail and eCommerce fraud can manifest in several different ways:

Catfishing: bad actors will contact a company’s customer service department and manipulate employees, usually with a sad or sympathetic story, into waiving fees or giving them discounts or free products.

Bots: Automated attacks by hackers can acquire customer data, including credit card numbers, or take over accounts. Bots may also scoop up large quantities of limited-edition products, shutting out real customers. Especially for brands, ordering large quantities of goods and selling them on resale markets dilutes its reputation.

Inside jobs: Employee collusion in either stealing products, or helping outsiders abuse a company’s return and exchange policy.

However, all of these types of con jobs have something in common: a data trail. An organization can use that data to its advantage, with the right tools.